Dispersal-based indices and mapping of landscape connectivity
Bibliographic record
Abstract
Connectivity is often recommended as a coarse-filter indicator of landscape-level biodiversity, but useable measures of the concept for management applications are poorly developed. We describe a dispersal-based algorithm to index and map connectivity, modified from Richards et al. (2002). Users define hypothetical species with simple habitat and dispersal suitability models, home range sizes, and potential dispersal scales. Dispersal is simulated from suitable home ranges, with habitat-based declines in survivorship imposed with distance travelled. Indices include suitable home ranges, suitable home ranges encountered by dispersers, and a combined index of amount and connectivity of suitable habitat. Dispersal success and dispersers passing through each cell are mapped to help guide detailed landscape planning. We illustrate the connectivity algorithm with landscape scenarios simulated on a landscape in the North Thompson drainage of southern British Columbia. Compared to the simulated fire regime, clearcutting led to moderate declines in suitable home ranges and connectivity, clearcutting with Old-Growth Management Areas (OGMAS) produced a slight recovery by year 100, while partial cutting increased suitable habitat and dispersal. OGMAS and partial cuts better maintained some corridors. The connectivity algorithm, in conjunction with other indicators, is a useful tool for comparing planning scenarios, indexing progress over time, and guiding more detailed landscape planning.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".